Short-time critical dynamics and universality on a two-dimensional Triangular Lattice

Critical scaling and universality in short-time dynamics for spin models on a two-dimensional triangular lattice are investigated by using Monte Carlo simulation. Emphasis is placed on the dynamic evolution from fully ordered initialstates to show that universal scaling exists already in the short-time regime in form of power-law behavior of the magnetization and Binder cumulant. The results measured for the dynamic and static critical exponents, $\theta$, $z$, $\beta$ and $\nu$, confirm explicitly that the Potts models on the triangular lattice and square lattice belong to the same universality class. Our critical scaling analysis strongly suggests that the simulation for the dynamic relaxation can be used to determine numerically the universality.Comment: LaTex, 11 pages and 10 figures, to be published in Physica

Organizacao do banco de sementes botanicas do banco ativo de germoplasma de batata-doce, para conservacao do "pool genico" a longo prazo.

bitstream/item/109233/1/Organizacao-do-banco-de-sementes-botancias.pd

Microscopic Non-Universality versus Macroscopic Universality in Algorithms for Critical Dynamics

We study relaxation processes in spin systems near criticality after a quench from a high-temperature initial state. Special attention is paid to the stage where universal behavior, with increasing order parameter emerges from an early non-universal period. We compare various algorithms, lattice types, and updating schemes and find in each case the same universal behavior at macroscopic times, despite of surprising differences during the early non-universal stages.Comment: 9 pages, 3 figures, RevTeX, submitted to Phys. Rev. Let

Escaping Plato's Cave using Adversarial Training: 3D Shape From Unstructured 2D Image Collections

We introduce PLATONICGAN to discover the 3D structure of an object class from an unstructured collection of 2D images, i. e., neither any relation between the images is available nor additional information about the images is known. The key idea is to train a deep neural network to generate 3D shapes which rendered to images are indistinguishable from ground truth images (for a discriminator) under various camera models (i. e., rendering layers) and camera poses. Discriminating 2D images instead of 3D shapes allows tapping into unstructured 2D photo collections instead of relying on curated (e.g., aligned, annotated, etc.) 3D data sets. To establish constraints between 2D image observation and their 3D interpretation, we suggest a family of rendering layers that are effectively differentiable. This family includes visual hull, absorption-only (akin to x-ray), and emissionabsorption. We can successfully reconstruct 3D shapes from unstructured 2D images and extensively evaluate PLATONICGAN on a range of synthetic and real data sets achieving consistent improvements over baseline methods. We can also show that our method with additional 3D supervision further improves result quality and even surpasses the performance of 3D supervised methods

Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning

We show that denoising of 3D point clouds can be learned unsupervised, directly from noisy 3D point cloud data only. This is achieved by extending recent ideas from learning of unsupervised image denoisers to unstructured 3D point clouds. Unsupervised image denoisers operate under the assumption that a noisy pixel observation is a random realization of a distribution around a clean pixel value, which allows appropriate learning on this distribution to eventually converge to the correct value. Regrettably, this assumption is not valid for unstructured points: 3D point clouds are subject to total noise, i.e. deviations in all coordinates, with no reliable pixel grid. Thus, an observation can be the realization of an entire manifold of clean 3D points, which makes the quality of a naive extension of unsupervised image denoisers to 3D point clouds unfortunately only little better than mean filtering. To overcome this, and to enable effective and unsupervised 3D point cloud denoising, we introduce a spatial prior term, that steers converges to the unique closest out of the many possible modes on the manifold. Our results demonstrate unsupervised denoising performance similar to that of supervised learning with clean data when given enough training examples - whereby we do not need any pairs of noisy and clean training data

First lattice evidence for a non-trivial renormalization of the Higgs condensate

General arguments related to ``triviality'' predict that, in the broken phase of $(\lambda\Phi^4)_4$ theory, the condensate re-scales by a factor $Z_{\phi}$ different from the conventional wavefunction-renormalization factor, $Z_{prop}$. Using a lattice simulation in the Ising limit we measure $Z_{\phi}=m^2 \chi$ from the physical mass and susceptibility and $Z_{prop}$ from the residue of the shifted-field propagator. We find that the two $Z$'s differ, with the difference increasing rapidly as the continuum limit is approached. Since $Z_{\phi}$ affects the relation of to the Fermi constant it can sizeably affect the present bounds on the Higgs mass.Comment: 10 pages, 3 figures, 1 table, Latex2